Descriptions

The western slope of the Oregon Cascades receives up to 3500 mm of precipitation annually, with a majority falling between the months of November-March. In this maritime climate, the partitioning of precipitation between rain and snow is highly sensitive to temperature. Climate models generally agree that winter temperatures in the Pacific Northwest will increase in the next few decades. In this model-based study we apply a classification system based upon rain-snow probability, seasonal precipitation variability, land cover, landscape position, and geology for sub-basins of the McKenzie River Basin. Using a “delta” approach, we apply monthly projected changes in temperature and precipitation to the meteorological data that forces a spatially distributed snow model. The model distributes precipitation over the landscape as rain or snow depending on grid cell temperature. The metric for rain-snow probability uses the dimensionless ratio of Snow Water Equivalent (SWE) to precipitation (P; with the ratio referred to as SWE/P hereafter). This metric minimizes the effects of variable precipitation, while still accounting for impacts of warmer temperatures on snowmelt. Combining SWE/P likelihood with landscape metrics provides a probabilistic approach characterizing sub-basins and their spatiotemporal responses to warmer temperatures.